JP6961806B2 - 保温材下腐食(cui)を識別するためのニューラルネットワークによるサーモグラフィ画像処理 - Google Patents
保温材下腐食(cui)を識別するためのニューラルネットワークによるサーモグラフィ画像処理 Download PDFInfo
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Description
102 構造物
105 パイプ
110 保温材
115 界面
122 パイプ表面
124 パイプ表面
130 赤外カメラ
135 プラットフォーム
137 サーモグラフファイル
140 コンピュータシステム
145 データベース
300 畳み込みニューラルネットワーク(CNN)
312 第1の階層レベル
314 第2の階層的な層
316 第3の階層的な層
322 第1の畳み込み段階
324 非線形関数段階
326 プーリング段階
332 畳み込み段階
334 非線形段階
336 プーリング段階
350 最終出力
410 画素値
420 畳み込み行列
430 出力行列
Claims (18)
- 第1の機械学習システムによって、CUI分類精度の第1の閾値までトレーニングされたフィルタを使用して構造物内の保温材下腐食(CUI)を識別するためのコンピュータ実装方法であって、
赤外放射センサを使用して前記構造物から捕捉されたサーモグラフと、前記構造物および環境条件に関連する追加のデータと、を受信することと、
前記第1の機械学習システムの出力と前記構造物および環境条件に関連する追加のデータとを、以前の状態から現在の状態までの情報を組み込む第2の機械学習システムに入力することと、
CUI分類精度の第2の閾値に達するまで、前記第1の機械学習システムの前記出力と前記追加のデータとの経時的な変化に応じてCUIを識別するように、前記第2の機械学習システムをトレーニングすることと、
前記第2の閾値に達した後、前記第1の機械学習システムおよび前記第2の機械学習システムを協調的に使用して、受信したサーモグラフおよび追加のデータに基づいて前記構造物内のCUIを識別することと、を含む、コンピュータ実装方法。 - 前記第1の機械学習システムが畳み込みニューラルネットワークを含む、請求項1に記載のコンピュータ実装方法。
- 前記畳み込みニューラルネットワークが複数の階層的な層を含み、各階層的な層が畳み込み段階、非線形関数段階、およびプーリング段階を含む、請求項2に記載のコンピュータ実装方法。
- 前記第2の機械学習システムが、リカレントニューラルネットワークを含む、請求項2に記載のコンピュータ実装方法。
- 前記追加のデータが、経時的に測定された、周囲温度、前記構造物の物理的特性、および気象条件を含む、請求項4に記載のコンピュータ実装方法。
- 前記第1の機械学習システムおよび前記第2の機械学習システムが、前記構造物の外部の物体からの赤外放射の反射に対する誤検出発見を認識するようにトレーニングされる、請求項5に記載のコンピュータ実装方法。
- CUIの識別が、水分を高い尤度で閉じ込める前記保温材下の前記構造物の脆弱な領域を識別することを含む、請求項1に記載のコンピュータ実装方法。
- 前記構造物の検査によって初期のCUI分類を検証することをさらに含み、検証が、パルス渦電流評価、目視検査、保温材除去、および壁薄肉化の超音波試験のうちの少なくとも2つを使用して実行される、請求項1に記載のコンピュータ実装方法。
- 前記サーモグラフデータおよび前記追加のデータを前処理して、カテゴリ変数を符号化し、かつ連続変数を正規化することをさらに含む、請求項1に記載のコンピュータ実装方法。
- 第1の機械学習システムによって、CUI分類精度の第1の閾値までトレーニングされたフィルタを使用して構造物内の保温材下腐食(CUI)を識別するためのシステムであって、
プロセッサ、メモリ、および通信モジュールを含むコンピュータシステムと、を備え、前記プロセッサが、
赤外放射センサを使用して前記構造物から捕捉されたサーモグラフと、前記構造物および環境条件に関連する追加のデータを受信するステップと、
前記第1の機械学習システムの出力と前記構造物および環境条件に関連する追加のデータとを、以前の状態から現在の状態までの情報を組み込む第2の機械学習システムに入力するステップと、
CUI分類精度の第2の閾値に達するまで、前記第1の機械学習システムの前記出力と前記追加のデータとの経時的な変化に応じてCUIを識別するように、前記第2の機械学習システムをトレーニングするステップと、
前記第1の閾値および前記第2の閾値に達した後、前記第1の機械学習システムおよび前記第2の機械学習システムを協調的に使用して、現在のサーモグラフおよび追加のデータに基づいて前記構造物内のCUIを識別するステップと、を実行するプログラムを実行するように構成されている、システム。 - 前記第1の機械学習システムが、畳み込みニューラルネットワークを含む、請求項10に記載のシステム。
- 前記畳み込みニューラルネットワークが、複数の階層的な層を含み、各階層的な層は、畳み込み段階、非線形関段階、およびプーリング段階を含む、請求項11に記載のシステム。
- 前記第2の機械学習システムが、リカレントニューラルネットワークを含む、請求項10に記載のシステム。
- 前記追加のデータが、経時的に測定された、周囲温度、前記構造物の物理的特性、および気象条件を含む、請求項10に記載のシステム。
- 前記第1の機械学習システムおよび前記第2の機械学習システムが、前記構造物の外部の物体からの赤外放射の反射に対する誤検出発見を認識するようにトレーニングされる、請求項10に記載のシステム。
- CUIの識別が、水分を高い尤度で閉じ込める前記保温材下の前記構造物の脆弱な領域を識別することを含む、請求項10に記載のシステム。
- 前記プロセッサが、前記構造物の検査によって初期のCUI分類を検証するステップをさらに実行する前記プログラムを実行するように構成され、検証が、パルス渦電流評価、目視検査、保温材除去、および壁薄肉化の超音波試験のうちの少なくとも2つを使用して実行される、請求項10に記載のシステム。
- 前記コンピュータシステムが、前記サーモグラフデータおよび前記追加のデータを前処理して、カテゴリ変数を符号化し、かつ連続変数を正規化する、請求項10に記載のシステム。
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US15/712,490 | 2017-09-22 | ||
US15/712,490 US10551297B2 (en) | 2017-09-22 | 2017-09-22 | Thermography image processing with neural networks to identify corrosion under insulation (CUI) |
PCT/US2018/051865 WO2019060490A1 (en) | 2017-09-22 | 2018-09-20 | THERMOGRAPHIC IMAGE PROCESSING USING NEURAL NETWORKS TO IDENTIFY CORROSION UNDER INSULATION (CUI) |
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CN111094956A (zh) | 2020-05-01 |
US20200355601A1 (en) | 2020-11-12 |
CN111094956B (zh) | 2023-06-02 |
KR20200060372A (ko) | 2020-05-29 |
JP2021502543A (ja) | 2021-01-28 |
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